Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations220450
Missing cells239094
Missing cells (%)3.4%
Duplicate rows15636
Duplicate rows (%)7.1%
Total size in memory55.5 MiB
Average record size in memory264.0 B

Variable types

Categorical16
Numeric14
Text1
Unsupported1

Alerts

Dataset has 15636 (7.1%) duplicate rowsDuplicates
children is highly imbalanced (81.9%) Imbalance
babies is highly imbalanced (97.0%) Imbalance
meal is highly imbalanced (54.1%) Imbalance
distribution_channel is highly imbalanced (62.8%) Imbalance
is_repeated_guest is highly imbalanced (80.0%) Imbalance
reserved_room_type is highly imbalanced (59.8%) Imbalance
assigned_room_type is highly imbalanced (51.6%) Imbalance
deposit_type is highly imbalanced (63.8%) Imbalance
required_car_parking_spaces is highly imbalanced (85.2%) Imbalance
agent has 30206 (13.7%) missing values Missing
company has 207847 (94.3%) missing values Missing
adults is highly skewed (γ1 = 23.85907871) Skewed
previous_cancellations is highly skewed (γ1 = 20.25122199) Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 24.34646594) Skewed
reservation_status_date is an unsupported type, check if it needs cleaning or further analysis Unsupported
lead_time has 12397 (5.6%) zeros Zeros
stays_in_weekend_nights has 97084 (44.0%) zeros Zeros
stays_in_week_nights has 14064 (6.4%) zeros Zeros
previous_cancellations has 203331 (92.2%) zeros Zeros
previous_bookings_not_canceled has 214322 (97.2%) zeros Zeros
booking_changes has 187752 (85.2%) zeros Zeros
days_in_waiting_list has 212514 (96.4%) zeros Zeros
adr has 4053 (1.8%) zeros Zeros
total_of_special_requests has 134849 (61.2%) zeros Zeros

Reproduction

Analysis started2025-04-10 08:56:51.258721
Analysis finished2025-04-10 08:57:33.692279
Duration42.43 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

hotel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
City Hotel
144795 
Resort Hotel
75655 

Length

Max length12
Median length10
Mean length10.686369
Min length10

Characters and Unicode

Total characters2355810
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCity Hotel
2nd rowCity Hotel
3rd rowCity Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
City Hotel 144795
65.7%
Resort Hotel 75655
34.3%

Length

2025-04-10T05:57:33.849330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:34.013988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hotel 220450
50.0%
city 144795
32.8%
resort 75655
 
17.2%

Most occurring characters

ValueCountFrequency (%)
t 440900
18.7%
o 296105
12.6%
e 296105
12.6%
220450
9.4%
H 220450
9.4%
l 220450
9.4%
C 144795
 
6.1%
i 144795
 
6.1%
y 144795
 
6.1%
R 75655
 
3.2%
Other values (2) 151310
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2355810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 440900
18.7%
o 296105
12.6%
e 296105
12.6%
220450
9.4%
H 220450
9.4%
l 220450
9.4%
C 144795
 
6.1%
i 144795
 
6.1%
y 144795
 
6.1%
R 75655
 
3.2%
Other values (2) 151310
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2355810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 440900
18.7%
o 296105
12.6%
e 296105
12.6%
220450
9.4%
H 220450
9.4%
l 220450
9.4%
C 144795
 
6.1%
i 144795
 
6.1%
y 144795
 
6.1%
R 75655
 
3.2%
Other values (2) 151310
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2355810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 440900
18.7%
o 296105
12.6%
e 296105
12.6%
220450
9.4%
H 220450
9.4%
l 220450
9.4%
C 144795
 
6.1%
i 144795
 
6.1%
y 144795
 
6.1%
R 75655
 
3.2%
Other values (2) 151310
 
6.4%

is_canceled
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
0
139205 
1
81245 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters220450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 139205
63.1%
1 81245
36.9%

Length

2025-04-10T05:57:34.191921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:34.305564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 139205
63.1%
1 81245
36.9%

Most occurring characters

ValueCountFrequency (%)
0 139205
63.1%
1 81245
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 139205
63.1%
1 81245
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 139205
63.1%
1 81245
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 139205
63.1%
1 81245
36.9%

lead_time
Real number (ℝ)

Zeros 

Distinct479
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.28062
Minimum0
Maximum737
Zeros12397
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:34.449207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median66
Q3158
95-th percentile320
Maximum737
Range737
Interquartile range (IQR)141

Descriptive statistics

Standard deviation106.38601
Coefficient of variation (CV)1.0401385
Kurtosis1.381721
Mean102.28062
Median Absolute Deviation (MAD)58
Skewness1.3054921
Sum22547762
Variance11317.983
MonotonicityNot monotonic
2025-04-10T05:57:34.655670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12397
 
5.6%
1 6607
 
3.0%
2 3878
 
1.8%
3 3498
 
1.6%
4 3220
 
1.5%
5 3052
 
1.4%
6 2744
 
1.2%
7 2428
 
1.1%
8 2166
 
1.0%
12 2143
 
1.0%
Other values (469) 178317
80.9%
ValueCountFrequency (%)
0 12397
5.6%
1 6607
3.0%
2 3878
 
1.8%
3 3498
 
1.6%
4 3220
 
1.5%
5 3052
 
1.4%
6 2744
 
1.2%
7 2428
 
1.1%
8 2166
 
1.0%
9 1840
 
0.8%
ValueCountFrequency (%)
737 3
 
< 0.1%
709 2
 
< 0.1%
629 17
 
< 0.1%
626 60
< 0.1%
622 17
 
< 0.1%
615 17
 
< 0.1%
608 17
 
< 0.1%
605 60
< 0.1%
601 17
 
< 0.1%
594 17
 
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2019
79264 
2016
56609 
2017
40612 
2018
21996 
2015
21969 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters881800
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2016
3rd row2016
4th row2016
5th row2015

Common Values

ValueCountFrequency (%)
2019 79264
36.0%
2016 56609
25.7%
2017 40612
18.4%
2018 21996
 
10.0%
2015 21969
 
10.0%

Length

2025-04-10T05:57:34.837187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:34.946114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2019 79264
36.0%
2016 56609
25.7%
2017 40612
18.4%
2018 21996
 
10.0%
2015 21969
 
10.0%

Most occurring characters

ValueCountFrequency (%)
2 220450
25.0%
0 220450
25.0%
1 220450
25.0%
9 79264
 
9.0%
6 56609
 
6.4%
7 40612
 
4.6%
8 21996
 
2.5%
5 21969
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 881800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 220450
25.0%
0 220450
25.0%
1 220450
25.0%
9 79264
 
9.0%
6 56609
 
6.4%
7 40612
 
4.6%
8 21996
 
2.5%
5 21969
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 881800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 220450
25.0%
0 220450
25.0%
1 220450
25.0%
9 79264
 
9.0%
6 56609
 
6.4%
7 40612
 
4.6%
8 21996
 
2.5%
5 21969
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 881800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 220450
25.0%
0 220450
25.0%
1 220450
25.0%
9 79264
 
9.0%
6 56609
 
6.4%
7 40612
 
4.6%
8 21996
 
2.5%
5 21969
 
2.5%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
October
27271 
August
26703 
September
26125 
July
22775 
May
17242 
Other values (7)
100334 

Length

Max length9
Median length7
Mean length6.1932502
Min length3

Characters and Unicode

Total characters1365302
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeptember
2nd rowSeptember
3rd rowMarch
4th rowApril
5th rowAugust

Common Values

ValueCountFrequency (%)
October 27271
12.4%
August 26703
12.1%
September 26125
11.9%
July 22775
10.3%
May 17242
7.8%
December 16582
7.5%
April 16498
7.5%
June 16211
7.4%
November 15924
7.2%
March 14615
6.6%
Other values (2) 20504
9.3%

Length

2025-04-10T05:57:35.128803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
october 27271
12.4%
august 26703
12.1%
september 26125
11.9%
july 22775
10.3%
may 17242
7.8%
december 16582
7.5%
april 16498
7.5%
june 16211
7.4%
november 15924
7.2%
march 14615
6.6%
Other values (2) 20504
9.3%

Most occurring characters

ValueCountFrequency (%)
e 215702
15.8%
r 149770
 
11.0%
u 112896
 
8.3%
b 98153
 
7.2%
t 80099
 
5.9%
a 60614
 
4.4%
y 60521
 
4.4%
m 58631
 
4.3%
c 58468
 
4.3%
J 47239
 
3.5%
Other values (16) 423209
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1365302
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 215702
15.8%
r 149770
 
11.0%
u 112896
 
8.3%
b 98153
 
7.2%
t 80099
 
5.9%
a 60614
 
4.4%
y 60521
 
4.4%
m 58631
 
4.3%
c 58468
 
4.3%
J 47239
 
3.5%
Other values (16) 423209
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1365302
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 215702
15.8%
r 149770
 
11.0%
u 112896
 
8.3%
b 98153
 
7.2%
t 80099
 
5.9%
a 60614
 
4.4%
y 60521
 
4.4%
m 58631
 
4.3%
c 58468
 
4.3%
J 47239
 
3.5%
Other values (16) 423209
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1365302
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 215702
15.8%
r 149770
 
11.0%
u 112896
 
8.3%
b 98153
 
7.2%
t 80099
 
5.9%
a 60614
 
4.4%
y 60521
 
4.4%
m 58631
 
4.3%
c 58468
 
4.3%
J 47239
 
3.5%
Other values (16) 423209
31.0%

arrival_date_week_number
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.961061
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:35.294833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q119
median32
Q341
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.562111
Coefficient of variation (CV)0.45265789
Kurtosis-0.92573271
Mean29.961061
Median Absolute Deviation (MAD)10
Skewness-0.27303218
Sum6604916
Variance183.93085
MonotonicityNot monotonic
2025-04-10T05:57:35.507511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 7097
 
3.2%
41 6797
 
3.1%
42 6729
 
3.1%
38 6693
 
3.0%
39 6464
 
2.9%
32 5842
 
2.7%
40 5822
 
2.6%
34 5791
 
2.6%
30 5728
 
2.6%
43 5635
 
2.6%
Other values (43) 157852
71.6%
ValueCountFrequency (%)
1 1286
 
0.6%
2 1648
0.7%
3 1777
0.8%
4 2068
0.9%
5 1969
0.9%
6 2255
1.0%
7 3194
1.4%
8 3234
1.5%
9 3184
1.4%
10 3195
1.4%
ValueCountFrequency (%)
53 4451
2.0%
52 2900
1.3%
51 2181
1.0%
50 3617
1.6%
49 4411
2.0%
48 3620
1.6%
47 4051
1.8%
46 3569
1.6%
45 4428
2.0%
44 5411
2.5%

arrival_date_day_of_month
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.78763
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:35.697191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7597191
Coefficient of variation (CV)0.55484701
Kurtosis-1.1902355
Mean15.78763
Median Absolute Deviation (MAD)8
Skewness-0.0015869541
Sum3480383
Variance76.73268
MonotonicityNot monotonic
2025-04-10T05:57:35.861484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
28 10017
 
4.5%
5 8470
 
3.8%
17 8358
 
3.8%
12 7692
 
3.5%
16 7662
 
3.5%
18 7658
 
3.5%
25 7624
 
3.5%
26 7579
 
3.4%
15 7512
 
3.4%
9 7511
 
3.4%
Other values (21) 140367
63.7%
ValueCountFrequency (%)
1 6484
2.9%
2 7270
3.3%
3 7075
3.2%
4 7013
3.2%
5 8470
3.8%
6 7025
3.2%
7 6887
3.1%
8 7325
3.3%
9 7511
3.4%
10 6653
3.0%
ValueCountFrequency (%)
31 4169
1.9%
30 7445
3.4%
29 3574
 
1.6%
28 10017
4.5%
27 6938
3.1%
26 7579
3.4%
25 7624
3.5%
24 7375
3.3%
23 6671
3.0%
22 6522
3.0%

stays_in_weekend_nights
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91920163
Minimum0
Maximum19
Zeros97084
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:36.013688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.99688745
Coefficient of variation (CV)1.0845144
Kurtosis7.2428249
Mean0.91920163
Median Absolute Deviation (MAD)1
Skewness1.3831452
Sum202638
Variance0.99378459
MonotonicityNot monotonic
2025-04-10T05:57:36.179676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 97084
44.0%
2 60901
27.6%
1 56108
25.5%
4 3375
 
1.5%
3 2364
 
1.1%
6 238
 
0.1%
5 164
 
0.1%
8 108
 
< 0.1%
7 48
 
< 0.1%
9 25
 
< 0.1%
Other values (7) 35
 
< 0.1%
ValueCountFrequency (%)
0 97084
44.0%
1 56108
25.5%
2 60901
27.6%
3 2364
 
1.1%
4 3375
 
1.5%
5 164
 
0.1%
6 238
 
0.1%
7 48
 
< 0.1%
8 108
 
< 0.1%
9 25
 
< 0.1%
ValueCountFrequency (%)
19 2
 
< 0.1%
18 3
 
< 0.1%
16 4
 
< 0.1%
14 4
 
< 0.1%
13 5
 
< 0.1%
12 8
 
< 0.1%
10 9
 
< 0.1%
9 25
 
< 0.1%
8 108
< 0.1%
7 48
< 0.1%

stays_in_week_nights
Real number (ℝ)

Zeros 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4786029
Minimum0
Maximum50
Zeros14064
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:36.455538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8963995
Coefficient of variation (CV)0.76510824
Kurtosis24.352696
Mean2.4786029
Median Absolute Deviation (MAD)1
Skewness2.8462936
Sum546408
Variance3.596331
MonotonicityNot monotonic
2025-04-10T05:57:36.659362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 63893
29.0%
1 56810
25.8%
3 39485
17.9%
5 19988
 
9.1%
4 17176
 
7.8%
0 14064
 
6.4%
6 2840
 
1.3%
10 1949
 
0.9%
7 1912
 
0.9%
8 1208
 
0.5%
Other values (25) 1125
 
0.5%
ValueCountFrequency (%)
0 14064
 
6.4%
1 56810
25.8%
2 63893
29.0%
3 39485
17.9%
4 17176
 
7.8%
5 19988
 
9.1%
6 2840
 
1.3%
7 1912
 
0.9%
8 1208
 
0.5%
9 420
 
0.2%
ValueCountFrequency (%)
50 2
 
< 0.1%
42 3
 
< 0.1%
41 2
 
< 0.1%
40 2
 
< 0.1%
35 2
 
< 0.1%
34 2
 
< 0.1%
33 3
 
< 0.1%
32 1
 
< 0.1%
30 8
< 0.1%
26 1
 
< 0.1%

adults
Real number (ℝ)

Skewed 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8499887
Minimum0
Maximum55
Zeros719
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:36.767535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.62476855
Coefficient of variation (CV)0.3377148
Kurtosis1623.8275
Mean1.8499887
Median Absolute Deviation (MAD)0
Skewness23.859079
Sum407830
Variance0.39033575
MonotonicityNot monotonic
2025-04-10T05:57:36.868253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 166178
75.4%
1 43168
 
19.6%
3 10227
 
4.6%
0 719
 
0.3%
4 110
 
< 0.1%
26 15
 
< 0.1%
27 6
 
< 0.1%
20 6
 
< 0.1%
5 6
 
< 0.1%
40 3
 
< 0.1%
Other values (4) 12
 
< 0.1%
ValueCountFrequency (%)
0 719
 
0.3%
1 43168
 
19.6%
2 166178
75.4%
3 10227
 
4.6%
4 110
 
< 0.1%
5 6
 
< 0.1%
6 3
 
< 0.1%
10 3
 
< 0.1%
20 6
 
< 0.1%
26 15
 
< 0.1%
ValueCountFrequency (%)
55 3
 
< 0.1%
50 3
 
< 0.1%
40 3
 
< 0.1%
27 6
 
< 0.1%
26 15
 
< 0.1%
20 6
 
< 0.1%
10 3
 
< 0.1%
6 3
 
< 0.1%
5 6
 
< 0.1%
4 110
< 0.1%

children
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size3.4 MiB
0.0
205877 
1.0
 
8214
2.0
 
6223
3.0
 
120
10.0
 
4

Length

Max length4
Median length3
Mean length3.0000181
Min length3

Characters and Unicode

Total characters661318
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 205877
93.4%
1.0 8214
 
3.7%
2.0 6223
 
2.8%
3.0 120
 
0.1%
10.0 4
 
< 0.1%
(Missing) 12
 
< 0.1%

Length

2025-04-10T05:57:37.003134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:37.110883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 205877
93.4%
1.0 8214
 
3.7%
2.0 6223
 
2.8%
3.0 120
 
0.1%
10.0 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 426319
64.5%
. 220438
33.3%
1 8218
 
1.2%
2 6223
 
0.9%
3 120
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 661318
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 426319
64.5%
. 220438
33.3%
1 8218
 
1.2%
2 6223
 
0.9%
3 120
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 661318
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 426319
64.5%
. 220438
33.3%
1 8218
 
1.2%
2 6223
 
0.9%
3 120
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 661318
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 426319
64.5%
. 220438
33.3%
1 8218
 
1.2%
2 6223
 
0.9%
3 120
 
< 0.1%

babies
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
0
218658 
1
 
1760
2
 
27
9
 
3
10
 
2

Length

Max length2
Median length1
Mean length1.0000091
Min length1

Characters and Unicode

Total characters220452
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 218658
99.2%
1 1760
 
0.8%
2 27
 
< 0.1%
9 3
 
< 0.1%
10 2
 
< 0.1%

Length

2025-04-10T05:57:37.233950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:37.329933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 218658
99.2%
1 1760
 
0.8%
2 27
 
< 0.1%
9 3
 
< 0.1%
10 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 218660
99.2%
1 1762
 
0.8%
2 27
 
< 0.1%
9 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 220452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 218660
99.2%
1 1762
 
0.8%
2 27
 
< 0.1%
9 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 220452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 218660
99.2%
1 1762
 
0.8%
2 27
 
< 0.1%
9 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 220452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 218660
99.2%
1 1762
 
0.8%
2 27
 
< 0.1%
9 3
 
< 0.1%

meal
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
BB
171514 
HB
28359 
SC
 
16520
Undefined
 
2128
FB
 
1929

Length

Max length9
Median length2
Mean length2.0675709
Min length2

Characters and Unicode

Total characters455796
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowSC
3rd rowSC
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 171514
77.8%
HB 28359
 
12.9%
SC 16520
 
7.5%
Undefined 2128
 
1.0%
FB 1929
 
0.9%

Length

2025-04-10T05:57:37.437085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:37.526315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bb 171514
77.8%
hb 28359
 
12.9%
sc 16520
 
7.5%
undefined 2128
 
1.0%
fb 1929
 
0.9%

Most occurring characters

ValueCountFrequency (%)
B 373316
81.9%
H 28359
 
6.2%
S 16520
 
3.6%
C 16520
 
3.6%
n 4256
 
0.9%
d 4256
 
0.9%
e 4256
 
0.9%
U 2128
 
0.5%
f 2128
 
0.5%
i 2128
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 455796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 373316
81.9%
H 28359
 
6.2%
S 16520
 
3.6%
C 16520
 
3.6%
n 4256
 
0.9%
d 4256
 
0.9%
e 4256
 
0.9%
U 2128
 
0.5%
f 2128
 
0.5%
i 2128
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 455796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 373316
81.9%
H 28359
 
6.2%
S 16520
 
3.6%
C 16520
 
3.6%
n 4256
 
0.9%
d 4256
 
0.9%
e 4256
 
0.9%
U 2128
 
0.5%
f 2128
 
0.5%
i 2128
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 455796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 373316
81.9%
H 28359
 
6.2%
S 16520
 
3.6%
C 16520
 
3.6%
n 4256
 
0.9%
d 4256
 
0.9%
e 4256
 
0.9%
U 2128
 
0.5%
f 2128
 
0.5%
i 2128
 
0.5%
Distinct177
Distinct (%)0.1%
Missing1029
Missing (%)0.5%
Memory size3.4 MiB
2025-04-10T05:57:37.854817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9906572
Min length2

Characters and Unicode

Total characters656213
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowBEL
2nd rowDEU
3rd rowESP
4th rowPRT
5th rowPRT
ValueCountFrequency (%)
prt 97933
44.6%
gbr 20412
 
9.3%
fra 18290
 
8.3%
esp 16456
 
7.5%
deu 12188
 
5.6%
ita 6768
 
3.1%
irl 5829
 
2.7%
bel 3893
 
1.8%
nld 3602
 
1.6%
bra 3583
 
1.6%
Other values (167) 30467
 
13.9%
2025-04-10T05:57:38.290937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 153017
23.3%
P 116732
17.8%
T 107988
16.5%
E 38218
 
5.8%
A 37318
 
5.7%
B 28534
 
4.3%
S 25487
 
3.9%
U 22198
 
3.4%
G 22165
 
3.4%
F 19201
 
2.9%
Other values (16) 85355
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 656213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 153017
23.3%
P 116732
17.8%
T 107988
16.5%
E 38218
 
5.8%
A 37318
 
5.7%
B 28534
 
4.3%
S 25487
 
3.9%
U 22198
 
3.4%
G 22165
 
3.4%
F 19201
 
2.9%
Other values (16) 85355
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 656213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 153017
23.3%
P 116732
17.8%
T 107988
16.5%
E 38218
 
5.8%
A 37318
 
5.7%
B 28534
 
4.3%
S 25487
 
3.9%
U 22198
 
3.4%
G 22165
 
3.4%
F 19201
 
2.9%
Other values (16) 85355
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 656213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 153017
23.3%
P 116732
17.8%
T 107988
16.5%
E 38218
 
5.8%
A 37318
 
5.7%
B 28534
 
4.3%
S 25487
 
3.9%
U 22198
 
3.4%
G 22165
 
3.4%
F 19201
 
2.9%
Other values (16) 85355
13.0%

market_segment
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Online TA
96581 
Offline TA/TO
48882 
Groups
40038 
Direct
22925 
Corporate
10216 
Other values (3)
 
1808

Length

Max length13
Median length9
Mean length9.054602
Min length6

Characters and Unicode

Total characters1996087
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOnline TA
2nd rowOnline TA
3rd rowOnline TA
4th rowDirect
5th rowDirect

Common Values

ValueCountFrequency (%)
Online TA 96581
43.8%
Offline TA/TO 48882
22.2%
Groups 40038
18.2%
Direct 22925
 
10.4%
Corporate 10216
 
4.6%
Complementary 1440
 
0.7%
Aviation 362
 
0.2%
Undefined 6
 
< 0.1%

Length

2025-04-10T05:57:38.920920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:39.047348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
online 96581
26.4%
ta 96581
26.4%
offline 48882
13.4%
ta/to 48882
13.4%
groups 40038
10.9%
direct 22925
 
6.3%
corporate 10216
 
2.8%
complementary 1440
 
0.4%
aviation 362
 
0.1%
undefined 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 243858
12.2%
O 194345
9.7%
T 194345
9.7%
e 181496
9.1%
i 169118
8.5%
l 146903
 
7.4%
A 145825
 
7.3%
145463
 
7.3%
f 97770
 
4.9%
r 84835
 
4.3%
Other values (16) 392129
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1996087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 243858
12.2%
O 194345
9.7%
T 194345
9.7%
e 181496
9.1%
i 169118
8.5%
l 146903
 
7.4%
A 145825
 
7.3%
145463
 
7.3%
f 97770
 
4.9%
r 84835
 
4.3%
Other values (16) 392129
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1996087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 243858
12.2%
O 194345
9.7%
T 194345
9.7%
e 181496
9.1%
i 169118
8.5%
l 146903
 
7.4%
A 145825
 
7.3%
145463
 
7.3%
f 97770
 
4.9%
r 84835
 
4.3%
Other values (16) 392129
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1996087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 243858
12.2%
O 194345
9.7%
T 194345
9.7%
e 181496
9.1%
i 169118
8.5%
l 146903
 
7.4%
A 145825
 
7.3%
145463
 
7.3%
f 97770
 
4.9%
r 84835
 
4.3%
Other values (16) 392129
19.6%

distribution_channel
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
TA/TO
180131 
Direct
27089 
Corporate
 
12916
GDS
 
299
Undefined
 
15

Length

Max length9
Median length5
Mean length5.354797
Min length3

Characters and Unicode

Total characters1180465
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA/TO
2nd rowTA/TO
3rd rowTA/TO
4th rowDirect
5th rowDirect

Common Values

ValueCountFrequency (%)
TA/TO 180131
81.7%
Direct 27089
 
12.3%
Corporate 12916
 
5.9%
GDS 299
 
0.1%
Undefined 15
 
< 0.1%

Length

2025-04-10T05:57:39.237029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:39.335952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 180131
81.7%
direct 27089
 
12.3%
corporate 12916
 
5.9%
gds 299
 
0.1%
undefined 15
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 360262
30.5%
/ 180131
15.3%
O 180131
15.3%
A 180131
15.3%
r 52921
 
4.5%
e 40035
 
3.4%
t 40005
 
3.4%
D 27388
 
2.3%
i 27104
 
2.3%
c 27089
 
2.3%
Other values (10) 65268
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1180465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 360262
30.5%
/ 180131
15.3%
O 180131
15.3%
A 180131
15.3%
r 52921
 
4.5%
e 40035
 
3.4%
t 40005
 
3.4%
D 27388
 
2.3%
i 27104
 
2.3%
c 27089
 
2.3%
Other values (10) 65268
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1180465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 360262
30.5%
/ 180131
15.3%
O 180131
15.3%
A 180131
15.3%
r 52921
 
4.5%
e 40035
 
3.4%
t 40005
 
3.4%
D 27388
 
2.3%
i 27104
 
2.3%
c 27089
 
2.3%
Other values (10) 65268
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1180465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 360262
30.5%
/ 180131
15.3%
O 180131
15.3%
A 180131
15.3%
r 52921
 
4.5%
e 40035
 
3.4%
t 40005
 
3.4%
D 27388
 
2.3%
i 27104
 
2.3%
c 27089
 
2.3%
Other values (10) 65268
 
5.5%

is_repeated_guest
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
0
213582 
1
 
6868

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters220450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 213582
96.9%
1 6868
 
3.1%

Length

2025-04-10T05:57:39.489359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:39.588631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 213582
96.9%
1 6868
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 213582
96.9%
1 6868
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 213582
96.9%
1 6868
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 213582
96.9%
1 6868
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 213582
96.9%
1 6868
 
3.1%

previous_cancellations
Real number (ℝ)

Skewed  Zeros 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12571105
Minimum0
Maximum26
Zeros203331
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:39.686766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0493607
Coefficient of variation (CV)8.3474028
Kurtosis451.78693
Mean0.12571105
Median Absolute Deviation (MAD)0
Skewness20.251222
Sum27713
Variance1.101158
MonotonicityNot monotonic
2025-04-10T05:57:39.878145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 203331
92.2%
1 16162
 
7.3%
2 226
 
0.1%
24 144
 
0.1%
3 132
 
0.1%
26 78
 
< 0.1%
25 75
 
< 0.1%
11 72
 
< 0.1%
19 57
 
< 0.1%
4 43
 
< 0.1%
Other values (5) 130
 
0.1%
ValueCountFrequency (%)
0 203331
92.2%
1 16162
 
7.3%
2 226
 
0.1%
3 132
 
0.1%
4 43
 
< 0.1%
5 32
 
< 0.1%
6 29
 
< 0.1%
11 72
 
< 0.1%
13 24
 
< 0.1%
14 42
 
< 0.1%
ValueCountFrequency (%)
26 78
< 0.1%
25 75
< 0.1%
24 144
0.1%
21 3
 
< 0.1%
19 57
 
< 0.1%
14 42
 
< 0.1%
13 24
 
< 0.1%
11 72
< 0.1%
6 29
 
< 0.1%
5 32
 
< 0.1%

previous_bookings_not_canceled
Real number (ℝ)

Skewed  Zeros 

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12007258
Minimum0
Maximum72
Zeros214322
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:40.014582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3664408
Coefficient of variation (CV)11.380124
Kurtosis817.78214
Mean0.12007258
Median Absolute Deviation (MAD)0
Skewness24.346466
Sum26470
Variance1.8671606
MonotonicityNot monotonic
2025-04-10T05:57:40.194984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 214322
97.2%
1 2671
 
1.2%
2 992
 
0.4%
3 572
 
0.3%
4 389
 
0.2%
5 313
 
0.1%
6 191
 
0.1%
7 143
 
0.1%
8 114
 
0.1%
9 93
 
< 0.1%
Other values (63) 650
 
0.3%
ValueCountFrequency (%)
0 214322
97.2%
1 2671
 
1.2%
2 992
 
0.4%
3 572
 
0.3%
4 389
 
0.2%
5 313
 
0.1%
6 191
 
0.1%
7 143
 
0.1%
8 114
 
0.1%
9 93
 
< 0.1%
ValueCountFrequency (%)
72 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%

reserved_room_type
Categorical

Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
A
162415 
D
33035 
E
 
11350
F
 
5071
G
 
3625
Other values (5)
 
4954

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters220450
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
A 162415
73.7%
D 33035
 
15.0%
E 11350
 
5.1%
F 5071
 
2.3%
G 3625
 
1.6%
B 2281
 
1.0%
C 1558
 
0.7%
H 1078
 
0.5%
P 19
 
< 0.1%
L 18
 
< 0.1%

Length

2025-04-10T05:57:40.360324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:40.474476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 162415
73.7%
d 33035
 
15.0%
e 11350
 
5.1%
f 5071
 
2.3%
g 3625
 
1.6%
b 2281
 
1.0%
c 1558
 
0.7%
h 1078
 
0.5%
p 19
 
< 0.1%
l 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 162415
73.7%
D 33035
 
15.0%
E 11350
 
5.1%
F 5071
 
2.3%
G 3625
 
1.6%
B 2281
 
1.0%
C 1558
 
0.7%
H 1078
 
0.5%
P 19
 
< 0.1%
L 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 162415
73.7%
D 33035
 
15.0%
E 11350
 
5.1%
F 5071
 
2.3%
G 3625
 
1.6%
B 2281
 
1.0%
C 1558
 
0.7%
H 1078
 
0.5%
P 19
 
< 0.1%
L 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 162415
73.7%
D 33035
 
15.0%
E 11350
 
5.1%
F 5071
 
2.3%
G 3625
 
1.6%
B 2281
 
1.0%
C 1558
 
0.7%
H 1078
 
0.5%
P 19
 
< 0.1%
L 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 162415
73.7%
D 33035
 
15.0%
E 11350
 
5.1%
F 5071
 
2.3%
G 3625
 
1.6%
B 2281
 
1.0%
C 1558
 
0.7%
H 1078
 
0.5%
P 19
 
< 0.1%
L 18
 
< 0.1%

assigned_room_type
Categorical

Imbalance 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
A
137866 
D
45811 
E
14070 
F
 
6821
G
 
4543
Other values (7)
 
11339

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters220450
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
A 137866
62.5%
D 45811
 
20.8%
E 14070
 
6.4%
F 6821
 
3.1%
G 4543
 
2.1%
B 4540
 
2.1%
C 4321
 
2.0%
H 1310
 
0.6%
I 668
 
0.3%
K 478
 
0.2%
Other values (2) 22
 
< 0.1%

Length

2025-04-10T05:57:40.615917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 137866
62.5%
d 45811
 
20.8%
e 14070
 
6.4%
f 6821
 
3.1%
g 4543
 
2.1%
b 4540
 
2.1%
c 4321
 
2.0%
h 1310
 
0.6%
i 668
 
0.3%
k 478
 
0.2%
Other values (2) 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 137866
62.5%
D 45811
 
20.8%
E 14070
 
6.4%
F 6821
 
3.1%
G 4543
 
2.1%
B 4540
 
2.1%
C 4321
 
2.0%
H 1310
 
0.6%
I 668
 
0.3%
K 478
 
0.2%
Other values (2) 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 137866
62.5%
D 45811
 
20.8%
E 14070
 
6.4%
F 6821
 
3.1%
G 4543
 
2.1%
B 4540
 
2.1%
C 4321
 
2.0%
H 1310
 
0.6%
I 668
 
0.3%
K 478
 
0.2%
Other values (2) 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 137866
62.5%
D 45811
 
20.8%
E 14070
 
6.4%
F 6821
 
3.1%
G 4543
 
2.1%
B 4540
 
2.1%
C 4321
 
2.0%
H 1310
 
0.6%
I 668
 
0.3%
K 478
 
0.2%
Other values (2) 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 137866
62.5%
D 45811
 
20.8%
E 14070
 
6.4%
F 6821
 
3.1%
G 4543
 
2.1%
B 4540
 
2.1%
C 4321
 
2.0%
H 1310
 
0.6%
I 668
 
0.3%
K 478
 
0.2%
Other values (2) 22
 
< 0.1%

booking_changes
Real number (ℝ)

Zeros 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21308233
Minimum0
Maximum21
Zeros187752
Zeros (%)85.2%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:40.728084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.63584996
Coefficient of variation (CV)2.9840576
Kurtosis88.245898
Mean0.21308233
Median Absolute Deviation (MAD)0
Skewness6.2374415
Sum46974
Variance0.40430517
MonotonicityNot monotonic
2025-04-10T05:57:40.926059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 187752
85.2%
1 23462
 
10.6%
2 6557
 
3.0%
3 1595
 
0.7%
4 626
 
0.3%
5 205
 
0.1%
6 103
 
< 0.1%
7 55
 
< 0.1%
8 29
 
< 0.1%
9 16
 
< 0.1%
Other values (11) 50
 
< 0.1%
ValueCountFrequency (%)
0 187752
85.2%
1 23462
 
10.6%
2 6557
 
3.0%
3 1595
 
0.7%
4 626
 
0.3%
5 205
 
0.1%
6 103
 
< 0.1%
7 55
 
< 0.1%
8 29
 
< 0.1%
9 16
 
< 0.1%
ValueCountFrequency (%)
21 2
 
< 0.1%
20 3
 
< 0.1%
18 1
 
< 0.1%
17 5
< 0.1%
16 3
 
< 0.1%
15 5
< 0.1%
14 7
< 0.1%
13 9
< 0.1%
12 4
< 0.1%
11 4
< 0.1%

deposit_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
No Deposit
191328 
Non Refund
28822 
Refundable
 
300

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2204500
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 191328
86.8%
Non Refund 28822
 
13.1%
Refundable 300
 
0.1%

Length

2025-04-10T05:57:41.084202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:41.158716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 191328
43.4%
deposit 191328
43.4%
non 28822
 
6.5%
refund 28822
 
6.5%
refundable 300
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 411478
18.7%
e 220750
10.0%
N 220150
10.0%
220150
10.0%
s 191328
8.7%
i 191328
8.7%
t 191328
8.7%
p 191328
8.7%
D 191328
8.7%
n 57944
 
2.6%
Other values (7) 117388
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2204500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 411478
18.7%
e 220750
10.0%
N 220150
10.0%
220150
10.0%
s 191328
8.7%
i 191328
8.7%
t 191328
8.7%
p 191328
8.7%
D 191328
8.7%
n 57944
 
2.6%
Other values (7) 117388
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2204500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 411478
18.7%
e 220750
10.0%
N 220150
10.0%
220150
10.0%
s 191328
8.7%
i 191328
8.7%
t 191328
8.7%
p 191328
8.7%
D 191328
8.7%
n 57944
 
2.6%
Other values (7) 117388
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2204500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 411478
18.7%
e 220750
10.0%
N 220150
10.0%
220150
10.0%
s 191328
8.7%
i 191328
8.7%
t 191328
8.7%
p 191328
8.7%
D 191328
8.7%
n 57944
 
2.6%
Other values (7) 117388
 
5.3%

agent
Real number (ℝ)

Missing 

Distinct333
Distinct (%)0.2%
Missing30206
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean84.16739
Minimum1
Maximum535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:41.298118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median14
Q3201
95-th percentile250
Maximum535
Range534
Interquartile range (IQR)192

Descriptive statistics

Standard deviation107.98789
Coefficient of variation (CV)1.2830134
Kurtosis-0.21732489
Mean84.16739
Median Absolute Deviation (MAD)13
Skewness1.0575014
Sum16012341
Variance11661.384
MonotonicityNot monotonic
2025-04-10T05:57:41.460476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 52933
24.0%
240 25658
11.6%
1 18250
 
8.3%
6 7044
 
3.2%
7 5998
 
2.7%
14 5990
 
2.7%
250 5040
 
2.3%
28 3105
 
1.4%
241 3050
 
1.4%
3 2893
 
1.3%
Other values (323) 60283
27.3%
(Missing) 30206
13.7%
ValueCountFrequency (%)
1 18250
 
8.3%
2 346
 
0.2%
3 2893
 
1.3%
4 133
 
0.1%
5 760
 
0.3%
6 7044
 
3.2%
7 5998
 
2.7%
8 2788
 
1.3%
9 52933
24.0%
10 495
 
0.2%
ValueCountFrequency (%)
535 3
 
< 0.1%
531 68
< 0.1%
527 35
< 0.1%
526 8
 
< 0.1%
510 2
 
< 0.1%
509 10
 
< 0.1%
508 6
 
< 0.1%
502 24
 
< 0.1%
497 1
 
< 0.1%
495 57
< 0.1%

company
Real number (ℝ)

Missing 

Distinct352
Distinct (%)2.8%
Missing207847
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean176.09244
Minimum6
Maximum543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:41.638136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile40
Q147
median169
Q3238
95-th percentile405
Maximum543
Range537
Interquartile range (IQR)191

Descriptive statistics

Standard deviation124.11385
Coefficient of variation (CV)0.70482216
Kurtosis-0.44328574
Mean176.09244
Median Absolute Deviation (MAD)106
Skewness0.61107357
Sum2219293
Variance15404.248
MonotonicityNot monotonic
2025-04-10T05:57:41.831528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 1905
 
0.9%
223 1534
 
0.7%
45 495
 
0.2%
67 435
 
0.2%
281 399
 
0.2%
153 320
 
0.1%
174 294
 
0.1%
154 242
 
0.1%
233 227
 
0.1%
219 217
 
0.1%
Other values (342) 6535
 
3.0%
(Missing) 207847
94.3%
ValueCountFrequency (%)
6 2
 
< 0.1%
8 3
 
< 0.1%
9 85
< 0.1%
10 2
 
< 0.1%
11 3
 
< 0.1%
12 27
 
< 0.1%
14 13
 
< 0.1%
16 11
 
< 0.1%
18 2
 
< 0.1%
20 109
< 0.1%
ValueCountFrequency (%)
543 2
 
< 0.1%
541 1
 
< 0.1%
539 2
 
< 0.1%
534 2
 
< 0.1%
531 1
 
< 0.1%
530 5
 
< 0.1%
528 2
 
< 0.1%
525 15
< 0.1%
523 19
< 0.1%
521 7
 
< 0.1%

days_in_waiting_list
Real number (ℝ)

Zeros 

Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6934543
Minimum0
Maximum391
Zeros212514
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:41.990623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.627174
Coefficient of variation (CV)6.9157195
Kurtosis160.05919
Mean2.6934543
Median Absolute Deviation (MAD)0
Skewness10.971006
Sum593772
Variance346.97163
MonotonicityNot monotonic
2025-04-10T05:57:42.176836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 212514
96.4%
58 492
 
0.2%
39 451
 
0.2%
44 278
 
0.1%
31 253
 
0.1%
87 240
 
0.1%
69 214
 
0.1%
35 191
 
0.1%
50 186
 
0.1%
46 183
 
0.1%
Other values (118) 5448
 
2.5%
ValueCountFrequency (%)
0 212514
96.4%
1 20
 
< 0.1%
2 8
 
< 0.1%
3 118
 
0.1%
4 45
 
< 0.1%
5 10
 
< 0.1%
6 38
 
< 0.1%
7 4
 
< 0.1%
8 11
 
< 0.1%
9 29
 
< 0.1%
ValueCountFrequency (%)
391 90
< 0.1%
379 30
 
< 0.1%
330 30
 
< 0.1%
259 20
 
< 0.1%
236 69
< 0.1%
224 20
 
< 0.1%
223 119
0.1%
215 42
 
< 0.1%
207 30
 
< 0.1%
193 2
 
< 0.1%

customer_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Transient
157149 
Transient-Party
52010 
Contract
 
10160
Group
 
1131

Length

Max length15
Median length9
Mean length10.34895
Min length5

Characters and Unicode

Total characters2281426
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 157149
71.3%
Transient-Party 52010
 
23.6%
Contract 10160
 
4.6%
Group 1131
 
0.5%

Length

2025-04-10T05:57:42.348236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:42.437406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
transient 157149
71.3%
transient-party 52010
 
23.6%
contract 10160
 
4.6%
group 1131
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 428478
18.8%
t 281489
12.3%
r 272460
11.9%
a 271329
11.9%
T 209159
9.2%
s 209159
9.2%
i 209159
9.2%
e 209159
9.2%
y 52010
 
2.3%
- 52010
 
2.3%
Other values (7) 87014
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2281426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 428478
18.8%
t 281489
12.3%
r 272460
11.9%
a 271329
11.9%
T 209159
9.2%
s 209159
9.2%
i 209159
9.2%
e 209159
9.2%
y 52010
 
2.3%
- 52010
 
2.3%
Other values (7) 87014
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2281426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 428478
18.8%
t 281489
12.3%
r 272460
11.9%
a 271329
11.9%
T 209159
9.2%
s 209159
9.2%
i 209159
9.2%
e 209159
9.2%
y 52010
 
2.3%
- 52010
 
2.3%
Other values (7) 87014
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2281426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 428478
18.8%
t 281489
12.3%
r 272460
11.9%
a 271329
11.9%
T 209159
9.2%
s 209159
9.2%
i 209159
9.2%
e 209159
9.2%
y 52010
 
2.3%
- 52010
 
2.3%
Other values (7) 87014
 
3.8%

adr
Real number (ℝ)

Zeros 

Distinct8876
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.911112
Minimum-6.38
Maximum5400
Zeros4053
Zeros (%)1.8%
Negative1
Negative (%)< 0.1%
Memory size3.4 MiB
2025-04-10T05:57:42.571434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile37
Q165
median90
Q3120
95-th percentile186
Maximum5400
Range5406.38
Interquartile range (IQR)55

Descriptive statistics

Standard deviation49.23318
Coefficient of variation (CV)0.50283547
Kurtosis1221.5127
Mean97.911112
Median Absolute Deviation (MAD)26.85
Skewness12.188218
Sum21584505
Variance2423.906
MonotonicityNot monotonic
2025-04-10T05:57:42.752977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 9618
 
4.4%
75 5164
 
2.3%
65 4858
 
2.2%
90 4483
 
2.0%
0 4053
 
1.8%
80 3242
 
1.5%
100 2856
 
1.3%
60 2844
 
1.3%
95 2719
 
1.2%
85 2678
 
1.2%
Other values (8866) 177935
80.7%
ValueCountFrequency (%)
-6.38 1
 
< 0.1%
0 4053
1.8%
0.26 1
 
< 0.1%
0.5 2
 
< 0.1%
1 29
 
< 0.1%
1.29 2
 
< 0.1%
1.48 2
 
< 0.1%
1.56 3
 
< 0.1%
1.6 3
 
< 0.1%
1.8 2
 
< 0.1%
ValueCountFrequency (%)
5400 2
< 0.1%
510 1
 
< 0.1%
508 3
< 0.1%
451.5 2
< 0.1%
450 1
 
< 0.1%
437 1
 
< 0.1%
426.25 1
 
< 0.1%
402 1
 
< 0.1%
397.38 1
 
< 0.1%
392 2
< 0.1%

required_car_parking_spaces
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
0
206492 
1
 
13906
2
 
46
3
 
4
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters220450
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 206492
93.7%
1 13906
 
6.3%
2 46
 
< 0.1%
3 4
 
< 0.1%
8 2
 
< 0.1%

Length

2025-04-10T05:57:42.900579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:42.994976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 206492
93.7%
1 13906
 
6.3%
2 46
 
< 0.1%
3 4
 
< 0.1%
8 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 206492
93.7%
1 13906
 
6.3%
2 46
 
< 0.1%
3 4
 
< 0.1%
8 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 206492
93.7%
1 13906
 
6.3%
2 46
 
< 0.1%
3 4
 
< 0.1%
8 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 206492
93.7%
1 13906
 
6.3%
2 46
 
< 0.1%
3 4
 
< 0.1%
8 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 220450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 206492
93.7%
1 13906
 
6.3%
2 46
 
< 0.1%
3 4
 
< 0.1%
8 2
 
< 0.1%

total_of_special_requests
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53800862
Minimum0
Maximum5
Zeros134849
Zeros (%)61.2%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-10T05:57:43.082414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.77661042
Coefficient of variation (CV)1.4434907
Kurtosis1.6078064
Mean0.53800862
Median Absolute Deviation (MAD)0
Skewness1.405831
Sum118604
Variance0.60312374
MonotonicityNot monotonic
2025-04-10T05:57:43.215564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 134849
61.2%
1 57986
26.3%
2 22856
 
10.4%
3 4192
 
1.9%
4 505
 
0.2%
5 62
 
< 0.1%
ValueCountFrequency (%)
0 134849
61.2%
1 57986
26.3%
2 22856
 
10.4%
3 4192
 
1.9%
4 505
 
0.2%
5 62
 
< 0.1%
ValueCountFrequency (%)
5 62
 
< 0.1%
4 505
 
0.2%
3 4192
 
1.9%
2 22856
 
10.4%
1 57986
26.3%
0 134849
61.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Check-Out
139295 
Canceled
78829 
No-Show
 
2326

Length

Max length9
Median length9
Mean length8.6213155
Min length7

Characters and Unicode

Total characters1900569
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCanceled
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out 139295
63.2%
Canceled 78829
35.8%
No-Show 2326
 
1.1%

Length

2025-04-10T05:57:43.397839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T05:57:43.500737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
check-out 139295
63.2%
canceled 78829
35.8%
no-show 2326
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 296953
15.6%
C 218124
11.5%
c 218124
11.5%
h 141621
7.5%
- 141621
7.5%
u 139295
7.3%
t 139295
7.3%
O 139295
7.3%
k 139295
7.3%
a 78829
 
4.1%
Other values (7) 248117
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1900569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 296953
15.6%
C 218124
11.5%
c 218124
11.5%
h 141621
7.5%
- 141621
7.5%
u 139295
7.3%
t 139295
7.3%
O 139295
7.3%
k 139295
7.3%
a 78829
 
4.1%
Other values (7) 248117
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1900569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 296953
15.6%
C 218124
11.5%
c 218124
11.5%
h 141621
7.5%
- 141621
7.5%
u 139295
7.3%
t 139295
7.3%
O 139295
7.3%
k 139295
7.3%
a 78829
 
4.1%
Other values (7) 248117
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1900569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 296953
15.6%
C 218124
11.5%
c 218124
11.5%
h 141621
7.5%
- 141621
7.5%
u 139295
7.3%
t 139295
7.3%
O 139295
7.3%
k 139295
7.3%
a 78829
 
4.1%
Other values (7) 248117
13.1%

reservation_status_date
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.4 MiB

Interactions

2025-04-10T05:57:26.086059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:56:58.460366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:00.709227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:02.719900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:05.174011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:07.278746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:09.231971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:11.238155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:13.290043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:15.605284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:17.596985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:19.659701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:21.485595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:23.702618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:26.254699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:56:58.656721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:00.868865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:02.854990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:05.318768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:07.411373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:09.383118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:11.408201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:13.416005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:15.738947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:17.743265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:19.783400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:21.652171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:23.840005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:26.394424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:56:58.799442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:01.002797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:02.990401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:05.463357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:07.552252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:09.516865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:11.567462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:13.560409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:15.908523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:17.887146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:19.918453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:21.809517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:24.289276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:26.532709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:56:58.956883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:01.152945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:03.143882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:05.642171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:07.705261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:09.669148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:11.725655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:13.743163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:16.057105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:18.030452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:20.045855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:21.970360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:24.426220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:26.672151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:56:59.097407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:01.319291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:03.316010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:05.832972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:07.847337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:09.830425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:11.883125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:13.908386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:16.206241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:18.211171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:20.167579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:22.124790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:24.611813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:26.826624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:56:59.250133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:01.468837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:03.476787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:05.964765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:07.993087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:09.968402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:12.019931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:14.314379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:16.388095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:18.365217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:20.290295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:22.273645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:24.752306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:26.952596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:56:59.400971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:01.599035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:03.620497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:06.100644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:08.121266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:10.099123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:12.169132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:14.481254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:16.509790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:18.509952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:20.448885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:22.405986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:24.929789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:27.192264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:56:59.550476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:01.741366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:03.768367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:06.250620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:08.234554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:10.219945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:12.303998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:14.608442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:16.629494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:18.648386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:20.590972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:22.582758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:25.071949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:27.400254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:56:59.728304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:01.873753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:03.922270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:06.417831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:08.368662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:10.353181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:12.441282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:14.773954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:16.767885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:18.881992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:20.726910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:22.796865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:25.205013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:27.540070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:56:59.869109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:01.991259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:04.365590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:06.558388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:08.496919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:10.486866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:12.589039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:14.924709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:16.888792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:19.025768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:20.846318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:22.938406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:25.354888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:27.667039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:00.037534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:02.107497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:04.530184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:06.686141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:08.610543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:10.648037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:12.716707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:15.040559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:17.016825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:19.136276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:20.982642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:23.053418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:25.484013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:27.826097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:00.214755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:02.257203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:04.709870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:06.835256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:08.775260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:10.820058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:12.877039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:15.202945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:17.156530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:19.264193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:21.111177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:23.212693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:25.641128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:27.989393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:00.389514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:02.451070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:04.866627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:07.020139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:08.908994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:10.966331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:13.015157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:15.342183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:17.317565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:19.413725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:21.225656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:23.382552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:25.800245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:28.145192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:00.545307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:02.592624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:05.016506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:07.137789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:09.065511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:11.109997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:13.165977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:15.470776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:17.462090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:19.539964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:21.337544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:23.563304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T05:57:25.934509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Missing values

2025-04-10T05:57:28.510703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-10T05:57:30.904126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-10T05:57:33.118701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
0City Hotel0212015September3610420.00BBBELOnline TATA/TO000AA2No Deposit9.0NaN0Transient105.000Check-Out2015-09-05
1City Hotel0202016September38121010.00SCDEUOnline TATA/TO000AA0No Deposit9.0NaN0Transient89.002Check-Out2016-09-13
2City Hotel022016March13240220.00SCESPOnline TATA/TO000AA0No Deposit9.0NaN0Transient134.001Check-Out2016-03-26
3Resort Hotel162016April17210120.00BBPRTDirectDirect000DD0No DepositNaNNaN0Transient73.000Canceled2016-04-18
4Resort Hotel0402015August34202320.00BBPRTDirectDirect000DD0No Deposit250.0NaN0Transient176.811Check-Out2015-08-25
5City Hotel02562017July29211220.00BBDEUOnline TATA/TO000AA0No Deposit9.0NaN0Transient-Party107.102Check-Out2017-07-24
6City Hotel1772015July29131220.00BBPRTOnline TATA/TO000AA0No Deposit9.0NaN0Transient76.501Canceled2015-06-29
7City Hotel012016August3240120.00BBBELOnline TATA/TO000AA0No Deposit9.0NaN0Transient151.001Check-Out2016-08-05
8City Hotel01502017April1422221.00BBFRAOnline TATA/TO000AA0No Deposit9.0NaN0Transient135.002Check-Out2017-04-06
9Resort Hotel0902017June26282520.00BBIRLDirectDirect000AA0No DepositNaNNaN0Transient127.000Check-Out2017-07-05
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
79254Resort Hotel1452019March1010520.00HBPRTGroupsDirect000EE0No DepositNaNNaN0Transient-Party81.0000Check-Out2019-01-20 00:00:00
79255Resort Hotel1452019March1010520.00HBPRTGroupsDirect000EE0No DepositNaNNaN0Transient-Party81.0000Check-Out2019-01-20 00:00:00
79256Resort Hotel1332019March1012620.00HBPRTGroupsDirect000AA0No DepositNaNNaN0Transient-Party65.0000Check-Out2019-02-25 00:00:00
79257Resort Hotel1572019March1012520.00BBPRTOnline TATA/TO000AA0No Deposit240.0NaN0Transient48.0001Check-Out2019-01-15 00:00:00
79258Resort Hotel1332019March1012610.00HBPRTGroupsDirect000AA0No DepositNaNNaN0Transient-Party50.0000Check-Out2019-02-25 00:00:00
79259Resort Hotel1612019March10141020.00BBPRTOffline TA/TOTA/TO000AA0No Deposit171.0NaN0Transient29.0000Check-Out2019-01-06 00:00:00
79260Resort Hotel12192019March1022520.00HBPRTOffline TA/TOTA/TO000AA0Non Refund310.0NaN0Transient52.0000Check-Out2018-11-20 00:00:00
79261Resort Hotel12192019March1022520.00HBCNOffline TA/TOTA/TO000AA0Non Refund310.0NaN0Transient52.0000Check-Out2018-11-20 00:00:00
79262Resort Hotel12192019March1022520.00HBPRTOffline TA/TOTA/TO000AA0Non Refund310.0NaN0Transient52.0000Check-Out2018-11-20 00:00:00
79263Resort Hotel11182019March1022520.00BBPRTOnline TATA/TO000AA0No Deposit241.0NaN0Transient33.2600Check-Out2019-01-27 00:00:00

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_status# duplicates
10238City Hotel12772016November4671220.00BBPRTGroupsTA/TO000AA0Non RefundNaNNaN0Transient100.000Canceled180
10242City Hotel12772019November4671220.00BBPRTGroupsTA/TO000AA0Non RefundNaNNaN0Transient100.000Canceled180
8100City Hotel1682016February8170220.00BBPRTGroupsTA/TO010AA0Non Refund37.0NaN0Transient75.000Canceled150
8113City Hotel1682019February8170220.00BBPRTGroupsTA/TO010AA0Non Refund37.0NaN0Transient75.000Canceled150
7500City Hotel1342018December5080210.00BBPRTOffline TA/TOTA/TO010AA0Non Refund19.0NaN0Transient90.000Canceled140
7504City Hotel1342019December5080210.00BBPRTOffline TA/TOTA/TO010AA0Non Refund19.0NaN0Transient90.000Canceled140
7487City Hotel1342015December5080210.00BBPRTOffline TA/TOTA/TO010AA0Non Refund19.0NaN0Transient90.000Canceled139
9649City Hotel11882019June25150210.00BBPRTOffline TA/TOTA/TO000AA0Non Refund119.0NaN39Transient130.000Canceled109
9645City Hotel11882016June25150210.00BBPRTOffline TA/TOTA/TO000AA0Non Refund119.0NaN39Transient130.000Canceled108
9333City Hotel11582016May22240210.00BBPRTGroupsTA/TO000AA0Non Refund37.0NaN31Transient130.000Canceled101